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modelscope/tests/pipelines/test_image_semantic_segmentation.py

55 lines
2.1 KiB
Python

import unittest
import cv2
import PIL
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.cv.image_utils import semantic_seg_masks_to_image
from modelscope.utils.logger import get_logger
from modelscope.utils.test_utils import test_level
class ImageSemanticSegmentationTest(unittest.TestCase):
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_image_semantic_segmentation_panmerge(self):
input_location = 'data/test/images/image_semantic_segmentation.jpg'
model_id = 'damo/cv_swinL_semantic-segmentation_cocopanmerge'
segmenter = pipeline(Tasks.image_segmentation, model=model_id)
result = segmenter(input_location)
draw_img = semantic_seg_masks_to_image(result[OutputKeys.MASKS])
cv2.imwrite('result.jpg', draw_img)
print('test_image_semantic_segmentation_panmerge DONE')
PIL_array = PIL.Image.open(input_location)
result = segmenter(PIL_array)
draw_img = semantic_seg_masks_to_image(result[OutputKeys.MASKS])
cv2.imwrite('result.jpg', draw_img)
print('test_image_semantic_segmentation_panmerge_from_PIL DONE')
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
def test_image_semantic_segmentation_vitadapter(self):
input_location = 'data/test/images/image_semantic_segmentation.jpg'
model_id = 'damo/cv_vitadapter_semantic-segmentation_cocostuff164k'
segmenter = pipeline(Tasks.image_segmentation, model=model_id)
result = segmenter(input_location)
draw_img = semantic_seg_masks_to_image(result[OutputKeys.MASKS])
cv2.imwrite('result.jpg', draw_img)
print('test_image_semantic_segmentation_vitadapter DONE')
PIL_array = PIL.Image.open(input_location)
result = segmenter(PIL_array)
draw_img = semantic_seg_masks_to_image(result[OutputKeys.MASKS])
cv2.imwrite('result.jpg', draw_img)
print('test_image_semantic_segmentation_vitadapter_from_PIL DONE')
if __name__ == '__main__':
unittest.main()